CN117312763B - Variable frequency household appliance performance visual supervision system and method based on cloud platform - Google Patents
Variable frequency household appliance performance visual supervision system and method based on cloud platform Download PDFInfo
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Abstract
The invention relates to the technical field of performance supervision, in particular to a variable frequency household appliance performance visual supervision system and method based on a cloud platform, comprising the following steps: acquiring initial equipment parameters of each variable frequency household appliance in the cloud platform, historical working records of each variable frequency household appliance and electrical performance parameters in each historical working record; analyzing the state change abnormality degree of the target equipment according to the equipment state values of all time nodes in the performance change curve, and analyzing the influence degree of the power consumption abnormality of the target equipment on the performance abnormality when the performance change is abnormal; constructing a device loss model of the target device; predicting an abnormality occurrence time node of the target device based on the device loss values of the abnormal devices; and analyzing the difference value between the abnormal occurrence time of the target equipment and the time threshold value based on the abnormal occurrence time node, and selectively processing the target equipment according to the difference value, so that the frequency conversion household appliance can be early warned in time, and the damage probability of the frequency conversion equipment is reduced.
Description
Technical Field
The invention relates to the technical field of performance supervision, in particular to a variable frequency household appliance performance visual supervision system and method based on a cloud platform.
Background
With the development of economy, the home appliance industry is also continuously developing. From traditional household appliances to variable frequency household appliances, the product form and function of the household appliance industry are continuously updated. Most of traditional household appliances use single-phase asynchronous motors, and the work of the motors is always in a short-time repeated state, so that the problems of frequent starting, high noise, short service life of the motors, poor temperature stability and the like are solved; the frequency conversion household appliance can greatly reduce the defects, and the rotating speed of the motor can be changed by changing the frequency of the power supply, so that the energy conservation and the efficiency improvement are realized, and the frequency conversion household appliance has a very high development prospect.
In the using process of the variable frequency household appliance, the instability of the equipment is gradually increased along with the increase of the working times and the working time of the variable frequency household appliance; frequent use can definitely cause unavoidable problems such as equipment abrasion, part aging and the like, and if the problems cannot be handled in time, normal operation of the variable frequency household appliance can be affected, and even failure shutdown is caused. Meanwhile, when the variable frequency household appliances are sold to consumers by manufacturers, a certain quality guarantee period often exists, and equipment damage in the quality guarantee period definitely causes a certain loss for the manufacturers, so that how to analyze the abnormal loss of the variable frequency household appliances by acquiring the historical working records of each user on the variable frequency household appliances in the cloud platform becomes a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a frequency conversion household appliance performance visual supervision system and method based on a cloud platform, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a frequency conversion household appliance performance visual supervision method based on a cloud platform comprises the following steps:
step S100: collecting initial equipment parameters of each variable frequency household appliance in the cloud platform to form an equipment information set; collecting historical working records of all the variable frequency home appliances in the equipment information set, and respectively forming a historical record set of each variable frequency home appliance; setting any variable frequency household appliance in the equipment information set as target equipment, and collecting electrical performance parameters of the target equipment in each historical working record according to the historical record set to respectively form electrical parameter sets of each historical working record;
each variable frequency household appliance in the steps belongs to the equipment with the same model, and works by different users;
the history work record in the steps comprises an electric quantity change process in the working process, a time node for opening and closing equipment and the like; the method is a whole process record from the time of equipment starting to the time of next equipment starting;
step S200: acquiring electrical performance parameters of target equipment in each historical working record, and constructing a performance change curve of the target equipment; analyzing the state change abnormality degree of the target equipment according to the equipment state values of all time nodes in the performance change curve; capturing all performance change curves with the state change abnormality degree larger than an abnormality threshold, acquiring the working time length and the real-time power consumption in the working time length of the history working record corresponding to the performance change curves, and analyzing the influence degree of the power consumption abnormality of the target equipment on the performance abnormality when the performance change is abnormal;
step S300: analyzing the equipment loss probability of each historical work record according to the influence degree and the state change abnormality degree of the target equipment, and constructing an equipment loss model of the target equipment;
step S400: setting each variable frequency household appliance with equipment information set known to generate equipment abnormality as target abnormal equipment; capturing all target abnormal equipment information similar to the state change abnormality degree of the target equipment by using a similarity algorithm to form an abnormal homomorphism set; acquiring the equipment loss value of each piece of abnormal equipment in the abnormal similar set according to the equipment loss model, and predicting an abnormal occurrence time node of the target equipment based on the equipment loss value of each piece of abnormal equipment;
step S500: analyzing the difference value between the abnormal occurrence time of the target equipment and the time threshold value based on the abnormal occurrence time node, and selectively processing the target equipment according to the difference value; and monitoring the working record of the target equipment in real time, and uploading the monitoring data to a database.
Further, step S200 includes:
step S210: capturing electrical performance parameters of the target device at each time node based on initial device parameter values of the target device and electrical performance parameters recorded at each historical work, wherein the electrical performance parameters include real-time voltage, temperature and vibration rate of the target device; respectively weighting the initial equipment parameter value/voltage, the initial equipment parameter value/temperature and the initial equipment parameter value/vibration rate to obtain equipment state values of all time nodes, and respectively constructing performance change curves of all historical working records;
the device state value X in the above step is obtained by weighting the initial device parameter value/voltage U1, the initial device parameter value/temperature U2, and the initial device parameter value/vibration rate U3, where x=a1xu1+u2+u2+a3xu3, and a 1, a 2, and a 3 represent weight values of 1/voltage value, 1/temperature value, and 1/vibration rate, respectively; the higher the voltage, temperature, vibration rate, the lower the electrical performance parameter of the target device, and the probability of loss to the target device increases;
step S220: acquiring a device state value X on each time node in each performance change curve to respectively obtain state change abnormality degree W=beta X corresponding to each performance change curve; wherein β represents a state impact value of the target device; when W is greater than the abnormality threshold γ1, it indicates that the performance change of the target device is abnormal; at the moment, acquiring a historical working record corresponding to each performance change curve when the state change abnormality degree W is greater than an abnormality threshold gamma 1, capturing real-time power consumption of target equipment in the historical working record in the process of stopping the next equipment starting when the equipment is started, and constructing to obtain power consumption change curves of the target equipment on each time node;
the power consumption change curve represents the whole power consumption change process of a target device at a time node of device opening operation, a time node of device closing ending operation and a time node of device opening preparation operation;
the device state values on the time nodes are obtained by weighting the electrical performance parameters of the historical work records, the state change abnormality degree of each historical work record is analyzed, and the real-time power consumption of the target device in the performance change abnormality is analyzed based on the state change abnormality degree, so that the analysis of the abnormal state of the target device is facilitated, and the subsequent analysis of the loss degree of the target device is facilitated;
step S230: sequentially acquiring coordinate values (x 1, y 1), (x 2, y 2) and (x 3, y 3) corresponding to each adjacent three time nodes in the power consumption change curve, wherein (x 1, y 1), (x 2, y 2) and (x 3, y 3) respectively represent coordinate values corresponding to any adjacent first, second and third time nodes in the power consumption change curve; respectively calculating slope values K1= (y 2-y 1)/(x 2-x 1) of the first time node and the second time node and slope values K2= (y 3-y 2)/(x 3-x 2) of the second time node and the third time node according to the coordinate values; based on the slope values K1 and K2, when the absolute value K2-K1 is more than phi 1 and theta is less than phi 2, the abnormal energy consumption change of the target equipment is indicated when the time nodes x 1-x 3 are generated, the corresponding energy consumption abnormal time length of the absolute value is obtained, namely the absolute value of the energy consumption is i 3-x2, wherein theta is an included angle formed by connecting lines of corresponding coordinate values of the first time node, the second time node and the third time node, and phi 1 and phi 2 are respectively an inclination comparison threshold value and an angle threshold value; summing the energy consumption abnormal time lengths of all the energy consumption changes in the power consumption change curve, and calculating to obtain an abnormal period as T;
step S240: capturing a time node with power consumption=0 in the power consumption change curve, and respectively calculating the working time t1 and the equipment closing time t2 of the target equipment in the power consumption change curve based on the time node; obtaining an abnormal period T of the power consumption change curve, and calculating based on the abnormal period T, the working time T1 and the equipment closing time T2 to obtain power consumption abnormal probability F=T/[ T1 (t1+t2) ] of the target equipment in the power consumption change curve, and obtaining the influence degree R=epsilon+F of the power consumption abnormality of the target equipment on the performance abnormality when the performance change is abnormal; where ε represents a target device performance impact factor;
in the above steps, power consumption=0 indicates that the target device is in the off state;
practice shows that based on the electricity consumption abnormality probability F in the steps, when the duty ratio of the abnormality period in the working time is larger, the equipment closing time is shorter, and the electricity consumption abnormality probability of the target equipment is larger, at the moment, the condition that the target equipment is influenced by the working equipment use condition to cause the abnormality of the electrical performance of the target equipment is shown, the electricity consumption condition belongs to natural factors, and the electricity consumption condition can be improved by adjusting the electricity consumption condition;
the coordinate values corresponding to the adjacent time nodes in the power consumption change curve are captured in sequence, the change degree of the slope and the included angle is analyzed, the abnormal period of the power consumption change curve is obtained, the power consumption abnormality probability of the target equipment is analyzed based on the abnormal period, the influence degree of the power consumption abnormality of the target equipment on the performance abnormality when the performance change is abnormal is analyzed, and the subsequent analysis of the loss degree of the target equipment is facilitated.
Further, step S300 includes:
step S310: based on the historical work records corresponding to each performance change curve when the state change abnormality degree W is larger than an abnormality threshold gamma 1, wherein the number of the corresponding historical work records is m, and the influence degree R of electricity use abnormality on the performance abnormality in the m historical work records is respectively obtained;
step S320: based on the historical work records corresponding to the performance change curves when the state change abnormality degree W is smaller than the abnormality threshold gamma 1, wherein the number of the corresponding historical work records is n, constructing a device loss model Y-delta sigma of the target device in the historical work records according to the state change abnormality degree W and the influence degree R of the target device m (W*R)+∑ n W；
The reason for the abnormal performance change in the n historical fault records in the step belongs to the abnormal electrical performance caused by unnatural factors, and is not the abnormal electricity consumption caused.
Further, step S400 includes:
step S410: setting each variable frequency household appliance with equipment information set known to generate equipment abnormality as target abnormal equipment; capturing all target abnormal devices similar to the state change abnormality degree of the target device by using a similarity algorithm to form an abnormality homomorphism set;
step S420: respectively acquiring time nodes d1 of the abnormality occurrence of each abnormal device in the abnormal similar set, and based on the time nodes d1, the abnormality occurrence time d1-d2 of each abnormal device are respectively calculated when the difference value between the device loss value of the target device and the device loss value of each abnormal device is smaller than the loss threshold sigma; at the moment, obtaining an abnormal occurrence time node d0+d1-d2 of the target equipment according to the average value prediction of the abnormal occurrence time; wherein d0 represents a time node when the target device is currently starting the m+n+1th work record;
by predicting the abnormal time of the target equipment, the method is favorable for early warning the target equipment in time and reduces the damage probability of the variable frequency equipment.
Further, step S500 includes:
step S510: based on the abnormal occurrence time node d0+d1-d2 of the target equipment, when d1-d2 is smaller than the duration threshold value rho, the target equipment is judged to be abnormal and timely fed back to related responsible personnel; otherwise, the normal operation of the target equipment is indicated;
step S520: and monitoring the working record of the target equipment in real time, and uploading the monitoring data to a database.
Visual supervisory systems of frequency conversion household electrical appliances performance, its characterized in that: the system comprises: the system comprises a data acquisition module, a database, an intelligent analysis module, a model construction module, an anomaly prediction module and a data supervision module;
acquiring initial equipment parameters of each variable frequency household appliance in the cloud platform through the data acquisition module to form an equipment information set; collecting historical working records of all the variable frequency home appliances in the equipment information set, and respectively forming a historical record set of each variable frequency home appliance; setting any variable frequency household appliance in the equipment information set as target equipment, and collecting electrical performance parameters of the target equipment in each historical working record according to the historical record set to respectively form electrical parameter sets of each historical working record;
storing all acquired data through the database;
acquiring electrical performance parameters of the target equipment in each historical working record through the intelligent analysis module, and constructing a performance change curve of the target equipment; analyzing the state change abnormality degree of the target equipment according to the equipment state values of all time nodes in the performance change curve; capturing all performance change curves with the state change abnormality degree larger than an abnormality threshold, acquiring the working time length and the real-time power consumption in the working time length of the history working record corresponding to the performance change curves, and analyzing the influence degree of the power consumption abnormality of the target equipment on the performance abnormality when the performance change is abnormal;
analyzing the equipment loss probability of each historical work record according to the influence degree and the state change abnormality degree of the target equipment by the model construction module, and constructing an equipment loss model of the target equipment;
setting each variable frequency household appliance with equipment information centralized known to be abnormal as target abnormal equipment through the abnormality prediction module; capturing all target abnormal equipment information similar to the state change abnormality degree of the target equipment by using a similarity algorithm to form an abnormal homomorphism set; acquiring the equipment loss value of each piece of abnormal equipment in the abnormal similar set according to the equipment loss model, and predicting an abnormal occurrence time node of the target equipment based on the equipment loss value of each piece of abnormal equipment;
analyzing the difference value between the abnormal occurrence time length and the time length threshold value of the target equipment based on the abnormal occurrence time node by the data supervision module, and selectively processing the target equipment according to the difference value; and monitoring the working record of the target equipment in real time, and uploading the monitoring data to a database.
Further, the data acquisition module comprises an information acquisition unit, a work record acquisition unit and a parameter acquisition unit;
the information acquisition unit is used for acquiring initial equipment parameters of each variable frequency household appliance in the cloud platform; the working record acquisition unit is used for acquiring historical working records of all variable frequency household appliances in the equipment information set; the parameter acquisition unit is used for acquiring electrical performance parameters of the target equipment in each historical working record.
Further, the intelligent analysis module comprises an abnormality analysis unit and a degree analysis unit;
the abnormality analysis unit is used for analyzing the state change abnormality degree of the target equipment according to the equipment state values of all time nodes in the performance change curve; the degree analysis unit is used for obtaining the working time length of the historical working record corresponding to the performance change curve and the real-time power consumption in the working time length, and analyzing the influence degree of the power consumption abnormality on the performance abnormality when the performance change of the target equipment is abnormal.
Further, the abnormality prediction module comprises a similar analysis unit and an abnormality prediction unit;
the similar analysis unit is used for capturing all target abnormal equipment information similar to the state change abnormal degree of the target equipment by using a similarity algorithm; the abnormality prediction unit is used for obtaining the equipment loss value of each abnormal equipment in the abnormal similar set according to the equipment loss model, and predicting an abnormality occurrence time node of the target equipment based on the equipment loss value of each abnormal equipment.
Further, the data supervision module comprises an adaptive processing unit and a data supervision unit;
the self-adaptive processing unit is used for analyzing the difference value between the abnormal occurrence time length and the time length threshold value of the target equipment based on the abnormal occurrence time node, and selectively processing the target equipment according to the difference value; the data supervision unit is used for monitoring the work record of the target equipment in real time and uploading the monitoring data to the database.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, the coordinate values corresponding to each adjacent time node in the power consumption change curve are captured in sequence, the change degree of the slope and the included angle is analyzed, the abnormal period of the power consumption change curve is obtained, and the power consumption abnormality probability of the target equipment is analyzed based on the abnormal period, so that the influence degree of the power consumption abnormality on the performance abnormality of the target equipment when the performance change abnormality is analyzed is facilitated, and the subsequent analysis of the loss degree of the target equipment is facilitated; by predicting the abnormal time of the target equipment, the method is favorable for early warning the target equipment in time and reduces the damage probability of the variable frequency equipment.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a variable frequency household appliance performance visualization supervisory system based on a cloud platform of the present invention;
fig. 2 is a flowchart of a frequency conversion household appliance performance visual supervision method based on a cloud platform.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions: visual supervisory systems of frequency conversion household electrical appliances performance, its characterized in that: the system comprises: the system comprises a data acquisition module, a database, an intelligent analysis module, a model construction module, an anomaly prediction module and a data supervision module;
the method comprises the steps that initial equipment parameters of all variable frequency household appliances in a cloud platform are collected through a data collection module, and an equipment information set is formed; collecting historical working records of all the variable frequency home appliances in the equipment information set, and respectively forming a historical record set of each variable frequency home appliance; setting any variable frequency household appliance in the equipment information set as target equipment, and collecting electrical performance parameters of the target equipment in each historical working record according to the historical record set to respectively form electrical parameter sets of each historical working record;
the data acquisition module comprises an information acquisition unit, a work record acquisition unit and a parameter acquisition unit;
the information acquisition unit is used for acquiring initial equipment parameters of each variable frequency household appliance in the cloud platform; the working record acquisition unit is used for acquiring historical working records of all variable frequency household appliances in the equipment information set; the parameter acquisition unit is used for acquiring electrical performance parameters of the target equipment in each historical working record.
Storing all acquired data through a database;
acquiring electrical performance parameters of the target equipment in each historical working record through an intelligent analysis module, and constructing a performance change curve of the target equipment; analyzing the state change abnormality degree of the target equipment according to the equipment state values of all time nodes in the performance change curve; capturing all performance change curves with the state change abnormality degree larger than an abnormality threshold, acquiring the working time length and the real-time power consumption in the working time length of the history working record corresponding to the performance change curves, and analyzing the influence degree of the power consumption abnormality of the target equipment on the performance abnormality when the performance change is abnormal;
the intelligent analysis module comprises an abnormality analysis unit and a degree analysis unit;
the abnormality analysis unit is used for analyzing the state change abnormality degree of the target equipment according to the equipment state values of all time nodes in the performance change curve; the degree analysis unit is used for obtaining the working time length of the historical working record corresponding to the performance change curve and the real-time power consumption in the working time length, and analyzing the influence degree of the power consumption abnormality on the performance abnormality when the performance change of the target equipment is abnormal.
Analyzing the equipment loss probability of each historical work record according to the influence degree and the state change abnormality degree of the target equipment by a model construction module, and constructing an equipment loss model of the target equipment;
setting each variable frequency household appliance with equipment information centralized known to generate equipment abnormality as target abnormal equipment through an abnormality prediction module; capturing all target abnormal equipment information similar to the state change abnormality degree of the target equipment by using a similarity algorithm to form an abnormal homomorphism set; acquiring the equipment loss value of each piece of abnormal equipment in the abnormal similar set according to the equipment loss model, and predicting an abnormal occurrence time node of the target equipment based on the equipment loss value of each piece of abnormal equipment;
the abnormality prediction module comprises a similar analysis unit and an abnormality prediction unit;
the similar analysis unit is used for capturing all target abnormal equipment information similar to the state change abnormal degree of the target equipment by using a similarity algorithm; the abnormality prediction unit is used for obtaining the equipment loss value of each abnormal equipment in the abnormal similar set according to the equipment loss model, and predicting an abnormality occurrence time node of the target equipment based on the equipment loss value of each abnormal equipment.
Analyzing the difference value between the abnormal occurrence time length and the time length threshold value of the target equipment based on the abnormal occurrence time node by the data supervision module, and selectively processing the target equipment according to the difference value; monitoring the working record of the target equipment in real time, and uploading the monitoring data to a database;
the data supervision module comprises an adaptive processing unit and a data supervision unit;
the self-adaptive processing unit is used for analyzing the difference value between the abnormal occurrence time length and the time length threshold value of the target equipment based on the abnormal occurrence time node, and selectively processing the target equipment according to the difference value; the data supervision unit is used for monitoring the work record of the target equipment in real time and uploading the monitoring data to the database.
Referring to fig. 2, the present invention provides the following technical solutions: a frequency conversion household appliance performance visual supervision method based on a cloud platform comprises the following steps:
step S100: collecting initial equipment parameters of each variable frequency household appliance in the cloud platform to form an equipment information set; collecting historical working records of all the variable frequency home appliances in the equipment information set, and respectively forming a historical record set of each variable frequency home appliance; setting any variable frequency household appliance in the equipment information set as target equipment, and collecting electrical performance parameters of the target equipment in each historical working record according to the historical record set to respectively form electrical parameter sets of each historical working record;
each variable frequency household appliance in the steps belongs to the equipment with the same model, and works by different users;
the history work record in the steps comprises an electric quantity change process in the working process, a time node for opening and closing equipment and the like; the method is a whole process record from the time of equipment starting to the time of next equipment starting;
step S200: acquiring electrical performance parameters of target equipment in each historical working record, and constructing a performance change curve of the target equipment; analyzing the state change abnormality degree of the target equipment according to the equipment state values of all time nodes in the performance change curve; capturing all performance change curves with the state change abnormality degree larger than an abnormality threshold, acquiring the working time length and the real-time power consumption in the working time length of the history working record corresponding to the performance change curves, and analyzing the influence degree of the power consumption abnormality of the target equipment on the performance abnormality when the performance change is abnormal;
step S200 includes:
step S210: capturing electrical performance parameters of the target device at each time node based on initial device parameter values of the target device and electrical performance parameters recorded at each historical work, wherein the electrical performance parameters include real-time voltage, temperature and vibration rate of the target device; respectively weighting the initial equipment parameter value/voltage, the initial equipment parameter value/temperature and the initial equipment parameter value/vibration rate to obtain equipment state values of all time nodes, and respectively constructing performance change curves of all historical working records;
the device state value X in the above step is obtained by weighting the initial device parameter value/voltage U1, the initial device parameter value/temperature U2, and the initial device parameter value/vibration rate U3, where x=a1xu1+u2+u2+a3xu3, and a 1, a 2, and a 3 represent weight values of 1/voltage value, 1/temperature value, and 1/vibration rate, respectively; the higher the voltage, temperature, vibration rate, the lower the electrical performance parameter of the target device, and the probability of loss to the target device increases;
step S220: acquiring a device state value X on each time node in each performance change curve to respectively obtain state change abnormality degree W=beta X corresponding to each performance change curve; wherein β represents a state impact value of the target device; when W is greater than the abnormality threshold γ1, it indicates that the performance change of the target device is abnormal; at the moment, acquiring a historical working record corresponding to each performance change curve when the state change abnormality degree W is greater than an abnormality threshold gamma 1, capturing real-time power consumption of target equipment in the historical working record in the process of stopping the next equipment starting when the equipment is started, and constructing to obtain power consumption change curves of the target equipment on each time node;
the power consumption change curve represents the whole power consumption change process of a target device at a time node of device opening operation, a time node of device closing ending operation and a time node of device opening preparation operation;
step S230: sequentially acquiring coordinate values (x 1, y 1), (x 2, y 2) and (x 3, y 3) corresponding to each adjacent three time nodes in the power consumption change curve, wherein (x 1, y 1), (x 2, y 2) and (x 3, y 3) respectively represent coordinate values corresponding to any adjacent first, second and third time nodes in the power consumption change curve; respectively calculating slope values K1= (y 2-y 1)/(x 2-x 1) of the first time node and the second time node and slope values K2= (y 3-y 2)/(x 3-x 2) of the second time node and the third time node according to the coordinate values; based on the slope values K1 and K2, when the absolute value K2-K1 is more than phi 1 and theta is less than phi 2, the abnormal energy consumption change of the target equipment is indicated when the time nodes x 1-x 3 are generated, the corresponding energy consumption abnormal time length of the absolute value is obtained, namely the absolute value of the energy consumption is i 3-x2, wherein theta is an included angle formed by connecting lines of corresponding coordinate values of the first time node, the second time node and the third time node, and phi 1 and phi 2 are respectively an inclination comparison threshold value and an angle threshold value; summing the energy consumption abnormal time lengths of all the energy consumption changes in the power consumption change curve, and calculating to obtain an abnormal period as T;
step S240: capturing a time node with power consumption=0 in the power consumption change curve, and respectively calculating the working time t1 and the equipment closing time t2 of the target equipment in the power consumption change curve based on the time node; obtaining an abnormal period T of the power consumption change curve, and calculating based on the abnormal period T, the working time T1 and the equipment closing time T2 to obtain power consumption abnormal probability F=T/[ T1 (t1+t2) ] of the target equipment in the power consumption change curve, and obtaining the influence degree R=epsilon+F of the power consumption abnormality of the target equipment on the performance abnormality when the performance change is abnormal; where ε represents a target device performance impact factor;
in the above steps, power consumption=0 indicates that the target device is in the off state;
practice shows that based on the electricity consumption abnormality probability F in the steps, when the duty ratio of the abnormality period in the working time is larger, the equipment closing time is shorter, and the electricity consumption abnormality probability of the target equipment is larger, at the moment, the condition that the target equipment is influenced by the working equipment use condition to cause the abnormality of the electrical performance of the target equipment is shown, the electricity consumption condition belongs to natural factors, and the electricity consumption condition can be improved by adjusting.
Step S300: analyzing the equipment loss probability of each historical work record according to the influence degree and the state change abnormality degree of the target equipment, and constructing an equipment loss model of the target equipment;
step S300 includes:
step S310: based on the historical work records corresponding to each performance change curve when the state change abnormality degree W is larger than an abnormality threshold gamma 1, wherein the number of the corresponding historical work records is 500, and the influence degree R of electricity use abnormality on the performance abnormality in m historical work records is respectively obtained;
step S320: based on the historical working records corresponding to the performance change curves when the state change abnormality degree W is smaller than the abnormality threshold gamma 1, wherein the number of the corresponding historical working records is 30, constructing a device loss model Y-delta sigma of the target device in the historical working records according to the state change abnormality degree W and the influence degree R of the target device 500 (W*R)+∑ 30 W；
The reason for the abnormal performance change in the n historical fault records in the step belongs to the abnormal electrical performance caused by unnatural factors, and is not the abnormal electricity consumption caused.
Step S400: setting each variable frequency household appliance with equipment information set known to generate equipment abnormality as target abnormal equipment; capturing all target abnormal equipment information similar to the state change abnormality degree of the target equipment by using a similarity algorithm to form an abnormal homomorphism set; acquiring the equipment loss value of each piece of abnormal equipment in the abnormal similar set according to the equipment loss model, and predicting an abnormal occurrence time node of the target equipment based on the equipment loss value of each piece of abnormal equipment;
step S400 includes:
step S410: setting each variable frequency household appliance with equipment information set known to generate equipment abnormality as target abnormal equipment; capturing all target abnormal devices similar to the state change abnormality degree of the target device by using a similarity algorithm to form an abnormality homomorphism set;
step S420: respectively acquiring time nodes d1 of the abnormality occurrence of each abnormal device in the abnormal similar set, and based on the time nodes d1, the abnormality occurrence time d1-d2 of each abnormal device are respectively calculated when the difference value between the device loss value of the target device and the device loss value of each abnormal device is smaller than the loss threshold sigma; at the moment, obtaining an abnormal occurrence time node d0+d1-d2 of the target equipment according to the average value prediction of the abnormal occurrence time; where d0 represents the current time node of the target device at the beginning of the m+n+1th work record.
Step S500: analyzing the difference value between the abnormal occurrence time of the target equipment and the time threshold value based on the abnormal occurrence time node, and selectively processing the target equipment according to the difference value; and monitoring the working record of the target equipment in real time, and uploading the monitoring data to a database.
Step S500 includes:
step S510: based on the abnormal occurrence time node d0+d1-d2 of the target equipment, when d1-d2 is smaller than the duration threshold value rho, the target equipment is judged to be abnormal and timely fed back to related responsible personnel; otherwise, the normal operation of the target equipment is indicated;
step S520: and monitoring the working record of the target equipment in real time, and uploading the monitoring data to a database.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A visual supervision method for the performance of variable frequency household appliances based on a cloud platform is characterized in that: the method comprises the following steps:
step S100: collecting initial equipment parameters of each variable frequency household appliance in the cloud platform to form an equipment information set; collecting historical working records of all the variable frequency home appliances in the equipment information set, and respectively forming a historical record set of each variable frequency home appliance; acquiring electrical performance parameters of each variable frequency household appliance in each historical working record according to the historical record set, and forming electrical parameter sets of each historical working record respectively;
step S200: constructing a performance change curve of each variable frequency household appliance according to the electrical performance parameters in each historical working record, and analyzing the state change abnormality degree of each variable frequency household appliance according to the performance change curve; acquiring real-time power consumption of each variable frequency household appliance in the performance change curve based on the state change abnormality degree, and analyzing the influence degree of power consumption abnormality of each variable frequency household appliance when the performance change is abnormal;
step S300: analyzing the equipment loss probability of each historical work record according to the influence degree and the state change abnormality degree of each variable frequency household appliance, and constructing an equipment loss model of each variable frequency household appliance;
step S400: setting each variable frequency household appliance with equipment information set known to generate equipment abnormality as target abnormal equipment, and acquiring an equipment loss value of the target abnormal equipment according to an equipment loss model; predicting abnormal occurrence time nodes of all variable frequency home appliances based on the equipment loss value of the target abnormal equipment;
step S500: selectively processing each variable frequency household appliance based on the abnormal occurrence time node of each variable frequency household appliance; monitoring the working records of all the variable frequency home appliances in real time, and uploading the monitoring data to a database;
in step S200, the specific steps of constructing a performance change curve of each variable frequency household appliance according to the electrical performance parameters in each historical working record, and analyzing the abnormal degree of the state change of each variable frequency household appliance according to the performance change curve include:
step S210: setting any variable frequency household appliance in the equipment information set as target equipment, and capturing the electrical performance parameters of the target equipment on each time node based on the initial equipment parameter value of the target equipment and the electrical performance parameters recorded in each historical working record, wherein the electrical performance parameters comprise real-time voltage, temperature and vibration rate of the target equipment; respectively weighting the initial equipment parameter value/voltage, the initial equipment parameter value/temperature and the initial equipment parameter value/vibration rate to obtain equipment state values of all time nodes, and respectively constructing performance change curves of all historical working records; wherein the initial device parameter value represents a fixed parameter value;
step S220: acquiring a device state value X on each time node in each performance change curve to respectively obtain state change abnormality degree W=beta X corresponding to each performance change curve; wherein β represents a state impact value of the target device; when W is greater than the abnormality threshold γ1, it indicates that the performance change of the target device is abnormal; at the moment, acquiring a historical working record corresponding to each performance change curve when the state change abnormality degree W is greater than an abnormality threshold gamma 1, capturing real-time power consumption of target equipment in the historical working record in the process of stopping the next equipment starting when the equipment is started, and constructing to obtain power consumption change curves of the target equipment on each time node;
in the step S200, the specific step of obtaining the real-time power consumption of each variable frequency household appliance in the performance change curve based on the abnormal degree of the state change, and analyzing the influence degree of the abnormal power consumption of each variable frequency household appliance when the performance change is abnormal includes:
step S201: sequentially acquiring coordinate values (x 1, y 1), (x 2, y 2) and (x 3, y 3) corresponding to each adjacent three time nodes in the power consumption change curve, wherein (x 1, y 1), (x 2, y 2) and (x 3, y 3) respectively represent coordinate values corresponding to any adjacent first, second and third time nodes in the power consumption change curve; respectively calculating slope values K1= (y 2-y 1)/(x 2-x 1) of the first time node and the second time node and slope values K2= (y 3-y 2)/(x 3-x 2) of the second time node and the third time node according to the coordinate values;
step S202: based on the slope values K1 and K2, when the absolute value K2-K1 is more than phi 1 and theta is less than phi 2, the abnormal energy consumption change of the target equipment is indicated when the time nodes x 1-x 3 are generated, the corresponding energy consumption abnormal time length of the absolute value is obtained, namely the absolute value of the energy consumption is i 3-x2, wherein theta is an included angle formed by connecting lines of corresponding coordinate values of the first time node, the second time node and the third time node, and phi 1 and phi 2 are respectively an inclination comparison threshold value and an angle threshold value; summing the energy consumption abnormal time lengths of all the energy consumption changes in the power consumption change curve, and calculating to obtain an abnormal period as T;
step S203: capturing a time node with power consumption=0 in the power consumption change curve, and respectively calculating the working time t1 and the equipment closing time t2 of the target equipment in the power consumption change curve based on the time node; calculating to obtain the electricity consumption abnormality probability F=T/[ t1 (t1+t2) ] of the target equipment in the electricity consumption change curve according to the abnormality period T, the working time T1 and the equipment closing time T2, and obtaining the influence degree R=epsilon×F of the electricity consumption abnormality of the target equipment on the performance abnormality when the performance change is abnormal; where ε represents a target device performance impact factor;
the step S300 includes:
step S310: based on the historical work records corresponding to each performance change curve when the state change abnormality degree W is larger than an abnormality threshold gamma 1, wherein the number of the corresponding historical work records is m, and the influence degree R of electricity use abnormality on the performance abnormality in the m historical work records is respectively obtained;
step S320: based on the historical work records corresponding to the performance change curves when the state change abnormality degree W is smaller than the abnormality threshold gamma 1, wherein the number of the corresponding historical work records is n, constructing a device loss model Y-delta sigma of the target device in the historical work records according to the state change abnormality degree W and the influence degree R of the target device m (W*R)+∑ n W；
The step S400 includes:
step S410: setting each variable frequency household appliance with equipment information set known to generate equipment abnormality as target abnormal equipment; capturing all target abnormal devices similar to the state change abnormality degree of the target device by using a similarity algorithm to form an abnormality homomorphism set;
step S420: respectively acquiring time nodes d1 of the abnormality occurrence of each abnormal device in the abnormal similar set, and based on the time nodes d1, the abnormality occurrence time d1-d2 of each abnormal device are respectively calculated when the difference value between the device loss value of the target device and the device loss value of each abnormal device is smaller than the loss threshold sigma; at the moment, obtaining an abnormal occurrence time node d0+d1-d2 of the target equipment according to the average value prediction of the abnormal occurrence time; where d0 represents the current time node of the target device at the beginning of the m+n+1th work record.
2. The cloud platform-based variable frequency home appliance performance visual supervision method is characterized by comprising the following steps of: the step S500 includes:
step S510: based on the abnormal occurrence time node d0+d1-d2 of the target equipment, when d1-d2 is smaller than the duration threshold value rho, the target equipment is judged to be abnormal and timely fed back to related responsible personnel; otherwise, the normal operation of the target equipment is indicated;
step S520: and monitoring the working record of the target equipment in real time, and uploading the monitoring data to a database.
3. A frequency conversion household electrical appliance performance visualization supervision system for implementing the frequency conversion household electrical appliance performance visualization supervision method based on a cloud platform as claimed in any one of claims 1-2, which is characterized in that: the system comprises: the system comprises a data acquisition module, a database, an intelligent analysis module, a model construction module, an anomaly prediction module and a data supervision module;
acquiring initial equipment parameters, historical working records and electrical performance parameters in each historical working record of each variable frequency household appliance in the cloud platform through the data acquisition module;
storing all acquired data through the database;
analyzing the influence degree of abnormal electricity consumption of each variable frequency household appliance when the performance change is abnormal through the intelligent analysis module;
constructing a device loss model of each variable frequency household appliance through the model construction module;
predicting abnormal occurrence time nodes of all the variable frequency home appliances through the abnormal prediction module;
and selectively processing each variable frequency household appliance based on the abnormal occurrence time node of each variable frequency household appliance through the data supervision module.
4. The variable frequency home appliance performance visualization supervisory system according to claim 3, wherein: the data acquisition module comprises an information acquisition unit, a work record acquisition unit and a parameter acquisition unit;
the information acquisition unit is used for acquiring initial equipment parameters of each variable frequency household appliance in the cloud platform; the working record acquisition unit is used for acquiring historical working records of all variable frequency household appliances in the equipment information set; the parameter acquisition unit is used for acquiring electrical performance parameters of the target equipment in each historical working record.
5. The variable frequency home appliance performance visualization supervisory system according to claim 3, wherein: the intelligent analysis module comprises an abnormality analysis unit and a degree analysis unit;
the abnormality analysis unit is used for analyzing the state change abnormality degree of the target equipment according to the equipment state values of all time nodes in the performance change curve; the degree analysis unit is used for obtaining the working time length of the historical working record corresponding to the performance change curve and the real-time power consumption in the working time length, and analyzing the influence degree of the power consumption abnormality on the performance abnormality when the performance change of the target equipment is abnormal.
6. The variable frequency home appliance performance visualization supervisory system according to claim 3, wherein: the abnormality prediction module comprises a similar analysis unit and an abnormality prediction unit;
the similar analysis unit is used for capturing all target abnormal equipment information similar to the state change abnormal degree of the target equipment by using a similarity algorithm; the abnormality prediction unit is used for obtaining the equipment loss value of each piece of abnormal equipment in the abnormal similar set according to the equipment loss model, and predicting an abnormality occurrence time node of the target equipment based on the equipment loss value of each piece of abnormal equipment;
the data supervision module comprises an adaptive processing unit and a data supervision unit;
the self-adaptive processing unit is used for analyzing the difference value between the abnormal occurrence time length and the time length threshold value of the target equipment based on the abnormal occurrence time node, and selectively processing the target equipment according to the difference value; the data supervision unit is used for monitoring the work record of the target equipment in real time and uploading the monitoring data to the database.
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